The use of location-aware applications and services in mobile environments has seen tremendous growth ever since APPLE added GPS capabilities to the iPhone 3G in 2008. For example, at APPLE's World Wide Developer Conference in 2014, it was announced that more than 600,000 iOS applications were using APPLE's location services API (Application Program Interface). Similarly, 100's of thousands of location-aware applications are available for ANDROID devices, and location service are provided by Microsoft for Window Phones, Surface tablets, and other mobile devices running Microsoft Windows operating systems.
Ideally, it is preferable to obtain the most accurate location for a mobile device as might be available for the given use context. GPS is a fundamental feature that is provided with most of today's mobile phones. To locate a device that has GPS support, a GPS receiver in the device receives signals from three or more GPS satellites, and calculates the location of the device using well-known positioning algorithms based on the amount of time it takes for a signal to be transmitted between a given GPS satellite and the device in combination with very accurate GPS location data. However, the use of GPS is generally restricted to outdoor environments where line of sight to GPS satellites is available. Moreover, there is a significant portion of mobile devices that do not have built-in GPS support, such as most tables, laptops, and notebooks.
Another technique for locating a mobile device is through the use of radio signal trilateration. Historically, E911 support for mobile phones was typically facilitated through the use of location determination using cell towers. Under this approach, the location of the cell towers are known, and radio signal strength measurements are obtained from multiple cell towers and used to trilaterate the location of the phone. This produces rather coarse location results that are impractical in today's mobile environments; for example, under E911 Phase 2, mobile network operators are required to provide the latitude and longitude of callers within 300 meters. By comparison, GPS may be accurate to within a few meters in some environments.
To add location capabilities to devices that either don't have GPS support and for locations for which GPS is unavailable, APPLE (and others) have deployed location services that employ Wi-Fi base stations (aka, access points) to trilaterate the location of a mobile device based on Received Signal String Indication (RSSI) measurements of access point broadcast signals obtained by the mobile device. A similar RSSI-based trilateration scheme is employed to determine the location of the device based on known (or at least projected to be known) location of Wi-Fi access points. This entails gathering the location of literally millions of Wi-Fi access points, which originally was accomplished by companies such as Skyhook through “wardriving,” under which GPS-equipped vehicles are driven along streets and used to identify the location of Wi-Fi access points by their broadcast MAC addresses. More recently, APPLE and GOOGLE have employed crowdsourcing techniques under which the APPLE and ANDROID devices themselves are used to generate the location of new Wi-Fi access points (and/or update the location of existing Wi-Fi access points). Under APPLE's approach, an iOS device has a local database that includes the location of thousands of Wi-Fi access points, using their MAC addresses as a key. iOS devices, including iPads without GPS support, are enabled to trilaterate the location of a new Wi-Fi access point using RSSI measurements and provide corresponding location information to APPLE's crowdsourced location database. Under GOOGLE's approach, the ANDROID device sends information pertaining to RSSI measurements obtained from the new Wi-Fi access points and (potentially) other access points, along with GPS position information (if available). The location of the new Wi-Fi access point is then determined by GOOGLE's location services servers and added to the location database.
The crowdsourced Wi-Fi access point approach works well in environments with relatively sparse (Wi-Fi access point) densities and for some inside environments (e.g., within a home or a wood-framed structure), but has several drawbacks for high-density environments and when within larger buildings. One drawback is that RSSI measurements are subject to attenuation and other effects, making Wi-Fi access points “appear” to be located at different locations than their actual locations. Another problem is that some Wi-Fi access points provide a stronger broadcast signal than others, making such access points appear relatively closer than they actually are.
A second Wi-Fi based location technique uses Time of Flight (ToF) technology, where a Wi-Fi device establishes a ToF session with several access points, usually 3-4 or more for high accuracy. The device keeps the session alive for as long as the indoor location is required with all of the access points and open new sessions to other access points as the device is moved. Generally, the ToF technology achieves high accuracy for indoor locations, but also demands high power consumption from the Wi-Fi core components. Using the ToF technology for long time periods can have a significant negative effect on the device's battery.
When a high and dense number of users are using ToF in very crowded places like malls, train stations, stadiums, etc. the total number of ToF sessions would be very high. Considering a limited number of access points deployed, each of the access points would have to maintain hundreds of ToF sessions in order to support all the ToF users. Maintaining this number of sessions isn't feasible due to network and channel limitations and therefore the indoor location user experience would be drastically diminished.
In addition, when there's a shortage of access points to provide for so many ToF users, the number of collisions between ToF users increases. This can cause the general noise (white noise) in the environment to rise and as a consequence the access points would demand from all of the ToF users to raise their transmitter power. This problem would make the usage in ToF technology in indoor crowded environments even more power consuming.